{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib.dates import DateFormatter, WeekdayLocator, DayLocator, MONDAY, date2num\n",
    "import numpy as np\n",
    "import os\n",
    "import warnings \n",
    "\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = 'F:\\HQData\\market_A\\\\'\n",
    "\n",
    "zcfw = path + '002459.csv'\n",
    "index = path +'index_SZZS.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Open</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>preclose</th>\n",
       "      <th>Volume</th>\n",
       "      <th>curvalue</th>\n",
       "      <th>signType</th>\n",
       "      <th>dkWarnType</th>\n",
       "      <th>bonusInfo</th>\n",
       "      <th>bonusInfoType</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-10-21</th>\n",
       "      <td>82.20</td>\n",
       "      <td>81.75</td>\n",
       "      <td>77.79</td>\n",
       "      <td>79.05</td>\n",
       "      <td>80.10</td>\n",
       "      <td>15436298.0</td>\n",
       "      <td>1.220276e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-20</th>\n",
       "      <td>85.02</td>\n",
       "      <td>79.98</td>\n",
       "      <td>78.40</td>\n",
       "      <td>80.10</td>\n",
       "      <td>78.48</td>\n",
       "      <td>22007673.0</td>\n",
       "      <td>1.806183e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-19</th>\n",
       "      <td>80.02</td>\n",
       "      <td>79.30</td>\n",
       "      <td>77.44</td>\n",
       "      <td>78.48</td>\n",
       "      <td>78.51</td>\n",
       "      <td>12881479.0</td>\n",
       "      <td>1.011830e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-18</th>\n",
       "      <td>78.51</td>\n",
       "      <td>71.41</td>\n",
       "      <td>71.41</td>\n",
       "      <td>78.51</td>\n",
       "      <td>71.37</td>\n",
       "      <td>22443395.0</td>\n",
       "      <td>1.702299e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-15</th>\n",
       "      <td>72.99</td>\n",
       "      <td>69.34</td>\n",
       "      <td>68.30</td>\n",
       "      <td>71.37</td>\n",
       "      <td>69.35</td>\n",
       "      <td>13507974.0</td>\n",
       "      <td>9.563695e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             High   Open    Low  Close  preclose      Volume      curvalue  \\\n",
       "Date                                                                         \n",
       "2021-10-21  82.20  81.75  77.79  79.05     80.10  15436298.0  1.220276e+09   \n",
       "2021-10-20  85.02  79.98  78.40  80.10     78.48  22007673.0  1.806183e+09   \n",
       "2021-10-19  80.02  79.30  77.44  78.48     78.51  12881479.0  1.011830e+09   \n",
       "2021-10-18  78.51  71.41  71.41  78.51     71.37  22443395.0  1.702299e+09   \n",
       "2021-10-15  72.99  69.34  68.30  71.37     69.35  13507974.0  9.563695e+08   \n",
       "\n",
       "            signType  dkWarnType bonusInfo  bonusInfoType  \n",
       "Date                                                       \n",
       "2021-10-21         0           0       NaN              0  \n",
       "2021-10-20         0           0       NaN              0  \n",
       "2021-10-19         0           0       NaN              0  \n",
       "2021-10-18         0           0       NaN              0  \n",
       "2021-10-15         0           0       NaN              0  "
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#df = pd.read_csv(zcfw, index_col='times')\n",
    "file = index\n",
    "file = zcfw\n",
    "if not os.path.exists(file):\n",
    "    print('do not exist')\n",
    "else:\n",
    "    df = pd.read_csv(file)\n",
    "    df.rename( columns={'times':'Date','openp':'Open', 'highp':'High','lowp':'Low',\"nowv\":'Close','curvol':\"Volume\"}, inplace=True)\n",
    "    df.set_index('Date', inplace=True)\n",
    "    df.index = pd.to_datetime(df.index)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "def candleVolume(df, candletitle='a', bartitle=''):\n",
    "    from matplotlib import pyplot as plt\n",
    "    from matplotlib.dates import DateFormatter, WeekdayLocator, DayLocator, MONDAY, date2num\n",
    "    from mpl_finance import candlestick_ohlc\n",
    "    import numpy as np\n",
    "    import warnings \n",
    "\n",
    "    warnings.filterwarnings('ignore')    \n",
    "    plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "    plt.rcParams['axes.unicode_minus'] = False\n",
    "    plt.figure(figsize=(16, 8))\n",
    "    \n",
    "    seriesData = df.copy()\n",
    "    \n",
    "    Date = [date2num(date) for date in seriesData.index]\n",
    "    seriesData.index = list(range(len(Date)))\n",
    "    seriesData['Date'] = Date\n",
    "    \n",
    "    listData = zip(seriesData.Date, seriesData.Open, seriesData.High, seriesData.Low, seriesData.Close)\n",
    "    ax1 = plt.subplot(211)\n",
    "    ax2 = plt.subplot(212)\n",
    "    for ax in ax1, ax2:\n",
    "        mondays = WeekdayLocator(MONDAY)\n",
    "        weekFormatter = DateFormatter('%Y%m%d')\n",
    "        ax.xaxis.set_major_locator(mondays)\n",
    "        ax.xaxis.set_minor_locator(DayLocator())\n",
    "        ax.xaxis.set_major_formatter(weekFormatter)\n",
    "        ax.grid(True)\n",
    "        \n",
    "    ax1.set_ylim(seriesData.Low.min()-1, seriesData.High.max() + 1)\n",
    "    ax1.set_ylabel('蜡烛图及收盘价线')\n",
    "    candlestick_ohlc(ax1,listData, width=0.7, colorup='r', colordown='g')\n",
    "    plt.setp(plt.gca().get_xticklabels(), \\\n",
    "            rotation=20, horizontalalignment='center')\n",
    "    ax1.autoscale_view()\n",
    "    ax1.set_title(candletitle)\n",
    "    \n",
    "    \n",
    "    ax1.plot(seriesData.Date, seriesData.Close, color='black', label='收盘价')\n",
    "    ax1.legend(loc='best')\n",
    "    \n",
    "    ax2.set_ylabel('成交量')\n",
    "    ax2.set_ylim(0, seriesData.Volume.max()*2)\n",
    "    ax2.bar(np.array(Date)[np.array(seriesData.Close >= seriesData.Open)],\n",
    "           height = seriesData.loc[:,'Volume'][np.array(seriesData.Close >= seriesData.Open)],\n",
    "           color='r', align='center')\n",
    "    ax2.bar(np.array(Date)[np.array(seriesData.Close< seriesData.Open)],\n",
    "           height = seriesData.loc[:,'Volume'][np.array(seriesData.Close < seriesData.Open)],\n",
    "           color='g', align='center')\n",
    "    ax2.set_title(bartitle)\n",
    "    return(plt.show())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Open</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>preclose</th>\n",
       "      <th>Volume</th>\n",
       "      <th>curvalue</th>\n",
       "      <th>signType</th>\n",
       "      <th>dkWarnType</th>\n",
       "      <th>bonusInfo</th>\n",
       "      <th>bonusInfoType</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-10-21</th>\n",
       "      <td>82.20</td>\n",
       "      <td>81.75</td>\n",
       "      <td>77.79</td>\n",
       "      <td>79.05</td>\n",
       "      <td>80.10</td>\n",
       "      <td>15436298.0</td>\n",
       "      <td>1.220276e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-20</th>\n",
       "      <td>85.02</td>\n",
       "      <td>79.98</td>\n",
       "      <td>78.40</td>\n",
       "      <td>80.10</td>\n",
       "      <td>78.48</td>\n",
       "      <td>22007673.0</td>\n",
       "      <td>1.806183e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-19</th>\n",
       "      <td>80.02</td>\n",
       "      <td>79.30</td>\n",
       "      <td>77.44</td>\n",
       "      <td>78.48</td>\n",
       "      <td>78.51</td>\n",
       "      <td>12881479.0</td>\n",
       "      <td>1.011830e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-18</th>\n",
       "      <td>78.51</td>\n",
       "      <td>71.41</td>\n",
       "      <td>71.41</td>\n",
       "      <td>78.51</td>\n",
       "      <td>71.37</td>\n",
       "      <td>22443395.0</td>\n",
       "      <td>1.702299e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-15</th>\n",
       "      <td>72.99</td>\n",
       "      <td>69.34</td>\n",
       "      <td>68.30</td>\n",
       "      <td>71.37</td>\n",
       "      <td>69.35</td>\n",
       "      <td>13507974.0</td>\n",
       "      <td>9.563695e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             High   Open    Low  Close  preclose      Volume      curvalue  \\\n",
       "Date                                                                         \n",
       "2021-10-21  82.20  81.75  77.79  79.05     80.10  15436298.0  1.220276e+09   \n",
       "2021-10-20  85.02  79.98  78.40  80.10     78.48  22007673.0  1.806183e+09   \n",
       "2021-10-19  80.02  79.30  77.44  78.48     78.51  12881479.0  1.011830e+09   \n",
       "2021-10-18  78.51  71.41  71.41  78.51     71.37  22443395.0  1.702299e+09   \n",
       "2021-10-15  72.99  69.34  68.30  71.37     69.35  13507974.0  9.563695e+08   \n",
       "\n",
       "            signType  dkWarnType bonusInfo  bonusInfoType  \n",
       "Date                                                       \n",
       "2021-10-21         0           0       NaN              0  \n",
       "2021-10-20         0           0       NaN              0  \n",
       "2021-10-19         0           0       NaN              0  \n",
       "2021-10-18         0           0       NaN              0  \n",
       "2021-10-15         0           0       NaN              0  "
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# from candle import candleVolume\n",
    "df1 = df[df.index> '2021-09-01']\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "candleVolume(df1, candletitle='蜡烛图', bartitle='成交量')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2629.000000\n",
       "mean       17.007231\n",
       "std        10.915661\n",
       "min         5.310000\n",
       "25%        11.090000\n",
       "50%        14.180000\n",
       "75%        18.140000\n",
       "max        80.100000\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Close = df.Close\n",
    "\n",
    "Close.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_value = 5\n",
    "max_value = 85\n",
    "gap = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BreakClose</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-10-21</th>\n",
       "      <td>80.0</td>\n",
       "      <td>79.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-20</th>\n",
       "      <td>85.0</td>\n",
       "      <td>80.10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            BreakClose  Close\n",
       "Date                         \n",
       "2021-10-21        80.0  79.05\n",
       "2021-10-20        85.0  80.10"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BreakClose = np.ceil(Close/ gap) * gap # 调整数据到区间\n",
    "BreakClose.name = 'BreakClose'\n",
    "pd.DataFrame({'BreakClose': BreakClose, \\\n",
    "             'Close':Close}).head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "volume = df.Volume\n",
    "PrcChange = Close.diff()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加个增大对应交易日期的成交量\n",
    "UpVol = volume.replace(volume[PrcChange > 0], 0)\n",
    "UpVol[0] = 0\n",
    "DownVol = volume.replace(volume[PrcChange <= 0], 0)\n",
    "DownVol[0] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 价格变化区间中的成交量之和\n",
    "def Voblock(vol):\n",
    "    return ([np.sum(vol[BreakClose == x]) for x in range(5, 90, gap)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [],
   "source": [
    "cumUpVol = Voblock(UpVol)\n",
    "cumDownVol = Voblock(DownVol)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00000000e+00, 0.00000000e+00],\n",
       "       [1.31573762e+09, 1.27132795e+09],\n",
       "       [2.03390774e+09, 2.21292662e+09],\n",
       "       [2.16579136e+09, 2.22882504e+09],\n",
       "       [5.67896572e+08, 5.57169596e+08],\n",
       "       [6.37544102e+08, 5.23633075e+08],\n",
       "       [4.64199975e+08, 6.70904843e+08],\n",
       "       [5.43971753e+08, 4.82739372e+08],\n",
       "       [3.24299961e+08, 2.42816396e+08],\n",
       "       [1.40345262e+08, 1.66431285e+08],\n",
       "       [1.41312954e+08, 6.88935430e+07],\n",
       "       [1.18973783e+08, 9.89560150e+07],\n",
       "       [1.69711360e+08, 1.34344653e+08],\n",
       "       [1.37552384e+08, 1.18500050e+08],\n",
       "       [7.55249580e+07, 3.46233820e+07],\n",
       "       [3.37376870e+07, 6.48274240e+07]])"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AllVol = np.array([cumUpVol, cumDownVol]).transpose()\n",
    "AllVol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "barh() missing 1 required positional argument: 'y'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-230-45cfc1df37e5>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msetp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_xticklabels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrotation\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m20\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhorizontalalignment\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'center'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;31m# 1.barh(bottom=range(min_value, max_value, gap), width=AllVol[:, 0], height=2, color = 'g', alpha = 0.2)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0max1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbarh\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbottom\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmin_value\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_value\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgap\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mAllVol\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m  \u001b[0mheight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mleft\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mAllVol\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'g'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: barh() missing 1 required positional argument: 'y'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax1 = ax.twiny()\n",
    "ax.plot(Close)\n",
    "\n",
    "ax.set_title('不同价格区间的累计成交量图')\n",
    "ax.set_ylim(min_value, max_value)\n",
    "ax.set_xlabel('时间')\n",
    "\n",
    "plt.setp(ax.get_xticklabels(), rotation = 20, horizontalalignment='center')\n",
    "ax1.barh(bottom=range(min_value, max_value, gap), width=AllVol[:, 0], height=2, color = 'g', alpha = 0.2)\n",
    "ax1.barh(bottom=range(min_value, max_value, gap), width=AllVol[:, 1],  height=2, left=AllVol[:, 0], color = 'g', alpha = 0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00000000e+00, 1.31573762e+09, 2.03390774e+09, 2.16579136e+09,\n",
       "       5.67896572e+08, 6.37544102e+08, 4.64199975e+08, 5.43971753e+08,\n",
       "       3.24299961e+08, 1.40345262e+08, 1.41312954e+08, 1.18973783e+08,\n",
       "       1.69711360e+08, 1.37552384e+08, 7.55249580e+07, 3.37376870e+07])"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AllVol[:, 0]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "成交量+均线 制定交易策略\n",
    "\n",
    "1. 获取成交量 volume， 计算5日平均数  VolSMA5  VolSMA10\n",
    "    VOLSMA = (VOLSMA5 + VOLSMA10) / 2\n",
    "2. 获取价格\n",
    "3. 根据成交量制定成交量信号， 当 volume > VolSMA 释放买入信号， 小于 卖出\n",
    "4. 均线交易信号  5日上穿20日， 买入； 向下穿破20日， 卖出\n",
    "5. 合并信号： 都买入时， 进场\n",
    "6） 评价交易策略\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2021-10-08    18361147.45\n",
       "2021-09-30    19473576.05\n",
       "2021-09-29    20841871.70\n",
       "2021-09-28    20963790.30\n",
       "2021-09-27    22300497.05\n",
       "Name: Volume, dtype: float64"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "volume = df.Volume\n",
    "\n",
    "#volsma5 = pd.rolling_apply(volume, 5, np.mean)\n",
    "volsma5 = volume.rolling(5).apply(np.mean)\n",
    "\n",
    "volsma10 = volume.rolling(10).apply(np.mean)\n",
    "\n",
    "volsma = ((volsma5 + volsma10)/2).dropna()\n",
    "volsma.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2021-10-08    1\n",
       "2021-09-30    1\n",
       "2021-09-29    1\n",
       "2021-09-28   -1\n",
       "2021-09-27    1\n",
       "Name: Volume, dtype: int32"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "volsignal = (volume[-len(volsma):] > volsma) * 1\n",
    "volsignal[volsignal == 0] = -1\n",
    "volsignal.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_volume_signal(df):\n",
    "    volume = df.Volume\n",
    "    \n",
    "    volsma5 = volume.rolling(5).apply(np.mean)\n",
    "    volsma10 = volume.rolling(10).apply(np.mean)\n",
    "    volsma = ((volsma5 + volsma10)/2).dropna()\n",
    "    \n",
    "    volsignal = (volume[-len(volsma):] > volsma) * 1\n",
    "    volsignal[volsignal == 0] = -1\n",
    "    return volsignal\n",
    "\n",
    "def get_close_signal(df):\n",
    "    close = df.Close\n",
    "    \n",
    "    closesma5 = close.rolling(5).apply(np.mean)\n",
    "    closesma10 = close.rolling(10).apply(np.mean)\n",
    "    closesma = ((closesma5 + closesma10)/2).dropna()\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dfsmas(df):\n",
    "    close = df.Close\n",
    "    closesma5 = close.rolling(5).apply(np.mean)\n",
    "    closesma20 = close.rolling(20).apply(np.mean)\n",
    "    closesma = ((closesma5 + closesma20)/2).dropna()\n",
    "    return (closesma, closesma5,closesma20 )\n",
    "\n",
    "# 定义向上突破\n",
    "\n",
    "def upbreak(Line, RefLine):\n",
    "    signal = np.all([Line > RefLine, Line.shift(1) < RefLine.shift(1) ], axis=0)\n",
    "    return (pd.Series(signal[1:], index=Line.index[1:]))\n",
    "\n",
    "def downbreak(Line, RefLine):\n",
    "    signal = np.all([Line < RefLine, Line.shift(1) > RefLine.shift(1)], axis=0)\n",
    "    return (pd.Series(signal[1:], index=Line.index[1:]))\n",
    "\n",
    "UpSMA = upbreak(closesma5[-len(closesma20):], closesma20) * 1\n",
    "DownSMA = downbreak(closesma5[-len(closesma20):], closesma20) * 1\n",
    "\n",
    "SMAsignal = UpSMA - DownSMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2620.000000\n",
       "mean       -0.093511\n",
       "std         1.047568\n",
       "min        -2.000000\n",
       "25%        -1.000000\n",
       "50%        -1.000000\n",
       "75%         1.000000\n",
       "max         2.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "volsignal =volsignal[-len(SMAsignal):]\n",
    "\n",
    "signal = volsignal + SMAsignal\n",
    "signal.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2010-08-11    0.0\n",
       "2010-08-12    0.0\n",
       "2010-08-13    0.0\n",
       "2010-08-16    0.0\n",
       "2010-08-17    1.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trade = signal.replace([2, -2,1,-1,0],[1,-1,0,0,0])\n",
    "trade = trade.shift(1)[1:]\n",
    "trade.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6379310344827587"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 求胜率\n",
    "ret = ((close - close.shift(1))/ close.shift(1))\n",
    "ret.name = 'stockRet'\n",
    "tradeRet = trade * ret\n",
    "tradeRet.name = 'tradeRet'\n",
    "\n",
    "winRate = len(tradeRet[tradeRet > 0]) / len(tradeRet[tradeRet != 0])\n",
    "winRate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.48633879781420764"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ret[ret > 0]) / len(ret[ret != 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x186fcd1a040>"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tgbA4KCuEXhPcHZHwYpKkhWiqknPQ71q44U13RyLaAPmeJkRTnEs3g1QCwt0diWgjJEkL0RTHN5nb2P7ujUO0GdLcIYS9Pv09bF1i7idNc2soou2QJC2EvU7tgHYXwLC7IDzW3dGINkKaO4Swl9UCHfrAZXPcHYloQyRJC2EvGV0o3EA+cULYS1skSQuXs+sTp5Rqr5SaoJTq4OyAhPBYWst6hcLlGr1wqJSKAj4BPgWeVUqN1VqfcXpkQrhC9lHIOVH3vqhEiEyoemy1gFKuiEqISvb07rgI+J3WerMtYQ8BVjk3LCFcoKwYXrgELKX1HzP4FgiLNSMMywpl7mjhco0maa31VwBKqdHApcCjzg5KCKfbshg+udfcH/cIxF9Sc/+hNbBjGWz7t3m84WlzmzjSdTEKgZ39pJVSCpgBZANlTo1ICFdY+QD4h5jJkYbfDb7n/Sl0GwXjH4HSQjjylWnqAOgy1PWxijbNriSttdbAb5VSjwHXAO9U7FNKzQHmAHTp0sUZMQrhWJYyKC+GMfPg8j80fGxACPSZ7Jq4hKhDo707lFL3K6VutT2MBHKq79dav6K1TtZaJ3fs2NEZMQrhOOv/Cv++ztwPDHNvLELYwZ4ueK8Atyil1gO+wOfODUkIJ9qyGDJ+gsRR0G20u6MRolH2XDjMBmRWc+EdtBX6XgnXLHJ3JELYRYZPibZFhnaLVkY+raJtkSQtWhn5tIq2RZK0aGXk0yraFknSopWRT6toW7QVkPk3ROshSVq0LVpLTVq0KrJ8lvBeualQWlBzm7VckrRoVSRJC8+mNXz7T9j3adPO6zYa1jxe9z7/4JbHJYSLSJIWnm3VXNj8krnfZZh955zcBqd/NPfHzoP23av2KR/onuLICIVwKknSwrNlHzO3d26FDj3tO+dvgyD7iLnf50qI7e+c2IRwAWmcE55NWyHuIvsTNFQlaDDTkQrRikmSFp6tOf2afQPMbf+pENnV8TEJ4ULS3CE8m7Y2fV3Bm9417dIj75M1CUWrJ0laeLbm1KR7jDE/QngBae4QHk4Gn4i2TT79wrPJXBuijZNPv/BskqRFGyeffuHZtEYmRBJtmVw4FO6VcwI2PAPFuXXvz/gJOvZ1bUxCeBBJ0sJ9inPhbxfZpg8FonvVPiY4CnqNd21cQngQSdLCfYqyTYLufx1c9wr4Bbg7IiE8jrRJC/cpKza3fa+SBC1EPSRJC/cpLzK3MnWoEPWS5g7hGAWZ8PIwKDjbhJO0uZFJkISolyRp4Rj7V0B+Olx6BwSG239eQKj980QL0QZJkhYNO7wOPr7HtB9bSsFqgcl/gUE/hw3Pwu7l5ri8kxCRYPbJpEZCOIwkadGwo1+bifeH3GqmAN35rlmWatf/4Pi3EBQBnS4yCbr/VEnQQjiYJGlRt+9ehT0fQtZhCI6Ea/5utofFwP6Vpvtcxz4w8l648Gr3xiqEF5MkLYwVD0D2UZjxFvj6wdYlpgmjY1/oOrzquMv/aH6EEC4hSVpAbip8+7K5n38aIuKhMAv6XgnXvuje2IRo46SftIBdy6vul+SbSY0KMyEk2n0xCSEAO2rSSqkIYBngCxQAM7TWpc4OTDjY6V3mol5sf9OMcXhd1b7tS6vuL54EPn5gKYHg9i4PUwhRkz3NHTOBZ7XWXyilXgYmAR85NyzhcP8YYW4X5MIXD8OP/625f9wjpvZcWmAe+/pD0lTXxiiEqKXRJK21fqnaw45AhvPCEU636GLIPAixSXCjrQatfE07tHSfE8Lj2H3hUCk1DIjSWm92YjyiOU58b5onfG2TFJ3cBj/+D7Sl9rExF5okPWgmRCW6NEwhRNPZlaSVUu2BRcC0OvbNAeYAdOnSxaHBCTtoDUummCR9vp7V5mHuPQnGzoO4Aa6LTQjRYvZcOAwA/gs8qLU+dv5+rfUrwCsAycnJ2uERioaVl5gEnTjKDCypENMP2l3gvriEEA5hT036NmAI8JBS6iHgZa31O84NS9itrNDc9p1Ss+YshPAK9lw4fBl42QWxiObY+a65lTmZhfBKMpiltVv1oLnt0Nu9cQghnEKGhbd2ygdG3FNzfg0hhNeQJO3Jso/CN38DS5lttGASRPcwIwE7DzE9O6zlVV3vhBBeR5K0J/vpY9jyBoTFmYmPqotNguAoc9/H3/WxCSFcQpK0J6tYTfu+XWb+5uyjZuj218+bmrW13HS96zHGrWEKIZxHkrQnKy82Q7Z9/c1k+2ExZnufye6NSwjhMtK7w5OVF4NfkLujEEK4kdSkPcm+lbDuCdOsoTWUFUFQO3dHJYRwI0nSnmTXe3Bqh1nQNTzObLtgsHtjEkK4lSRpd8k/UzWku0JBhpkAafpi98QkhPA4kqRdSWtTUz72DayaW/cx3VNcGZEQwsNJknalYxthyZVVj8cvgNCYmsckXObKiIQQHk6StKtYLfCvq8z9aa9Dx74Ql+TemIQQHk+StKusWwjaagafJE2TpaqEEHaRftKusOdDWP+UuX/dPyVBCyHsJknaFT79P3P7i48horN7YxFCtCrS3OFIeafg8Lra24tzYPhd0G20y0MSQrRukqQdpSQfnu1b//72PVwXixDCa0iSBrOYq662hq5fYNPbjfPTzW2PcTDlmZr7fHwhIqFlMQoh2iRJ0j+8CR/dVXPbpXfAlU817XkspeZ28Exo380xsQkh2jxJ0hl7zcomKba1ArcugQOr4KuO9Z8TlVg1MtDHFw58Dptta/XKBPxCCAeSJF1WAEERMOp35vG5U/DdK7D28eY9X2CY42ITQrR5bStJlxVXNUugYdNLcPQb8A+pOmbyU3DFk/U/R1GWWdZKW83jz2zd63pPgsG3QKL04BBCOE7bSdJn9sPLw8FaVnvfoJur7isFvg28LWExcMltVY+/fAxKcmHIL6DvlfWfJ4QQzdC6k3TeSdj3mWkHHjAdAkLqPzbzgEnQw+6E8E5mm38QDL4V/Fqw2vavN0DBGYhPbv5zCCFEPVpfki4thPduMwuzHt9Utf3ju8E/tP7zKmrQl90BkV0cF09UV/MjhBBO0PqSdOYBU3uO6W9G8HUZBgFhVf2UGxIeJ/2VhRCtSutJ0lv/BT/8C9K2mscTHoVe490bkxBCOFnrSNK7PzDNGWCWlyothI693RuTEEK4gOcn6bJi2Ph3c//OLdChl3vjEUIIF/L8qUp3vG2aONrFS4IWQrQ5diVppVSsUmqDs4Op02d/MLdz1rnl5YUQwp0abe5QSkUB/wIa6N/mQFYrZB+xzUqnTde5zskQ1sBcGkII4aXsaZO2ADOAD50WRWkhfPhbOLgaAkLN/BnVDfq5015aCCFa6p5l2xjTJ4afDXb8ykuNJmmtdR6Acua6fKnfw+7l5n5Jnrmd+pq59fWDXhOd99pCCNFE20/kkJlfwrgLYymzWPlw+0m6dXBOY0OLe3copeYAcwC6dGnmSL6OfeG6V8zQ6uAoMzilJUO1hRDCiX724jcAHF04hewCM2lbdFigU16rxUlaa/0K8ApAcnKybuTwuoXHwsAZLQ1FCCFcSmvN2XyTpDuEOqdi6fld8IQQwkPll5STWVACOK8mbXeS1lqnOCUCIYRoRXS19VBfXX+YLFtzR1SIc1Zlkpq0EELY6WBGPt0e/Kzy8d/XHKSw1AJAaKBzBnBLkhZCCDt9uD2t1rYnPv0JgJAAX6e8piRpIYSw07HMwlrbzpWUAxAsSVoIIdzrox0nK+9f2KldjX0Bvs5Jp5KkhRDCDueKa66P2jG8Zm8OZw34kyQthBB2+GBbzfboLu2DK+9PuaiT015XkrQQQthh/oe7azy+zjZPx63DuvLCzwc77XU9f9J/IYRws3KLtda2QQlRfHLXSHrGhDl1biNJ0kII0YiDZ/JrbfP1USR1jnD6a0uSFkKIBlitmk93Vk2f/NOjkyiz1q5ZO4u0SQsh2oTcwjKe+OynGsO67fHeD6ksWnMQgPlX9SM4wJd2Qc4ZAl4XqUkLIdqE8c99xZlzJZRbNA9f3c/u8+Z/uKvy/g3J8c4IrUFSkxZCtAmXJrYHYPuJ7CadV1xW1bQR7sIadAVJ0kIIr5NfUs6B9HMkPvApy39IBSDe1q/5h+M5JD7wKX3nr2j0eSzWqqaRr+8f45xgGyFJWgjhdZIeWcWE59YD8O/NxwA4e660xjHFZVZ+/+4OCkvL632enak5ADz2syTio0KcFG3DPCJJF5SUs+/0OUrKLe4ORQjRyp3OLa7xOCzQD6tV856tRl3dez+k8v622jPbVbjupY2A81ZdsYdHJOkv92ZwxfPrOV7HDFNCCNEUH+2omXQ7hgeSml0EQOfI4FrHv7T2UJ3PU73SGODnvlTpEUm6fYj5L3U0s5ATWU1L1CeyCrFam7e0ohDC+3x7OIueMWH0igkDYPkPaYz+61oAnpsxiKMLp/DM9IHER5mEnZZTxMmcolrP8+zn+yvvp/SJcUHkdfOMJG37KnH7m1sY9dRanl9d9eYUlpaTVscbCLD1WDajnlpL97mf0WPuZyz99pgkbCHasNJyK1/uzSAqxJ8vfnd5rf0dwkyumXZxPF/fP5bfpPQAYNOhTMosVjYfzqyc2H/v6XMAJHeNwtfHecO+G+MR/aQviAyq8fj51Qe4d3xvtNb0e3hVjX3dOoTy8NX9SIgKYdrLGyu3W6yah97fxfIf0njzl5c6bSkbIYTnevQTMwlSpm0F70eu7sefPt4DwGXd2tOtQ2iN4++b0JvXNhxhX/o5ej1U1dsjPiqEr/afISrEn5dmDnFR9HVTTR1905Dk5GS9ZcuWZp17Nr+EN74+wtHMAj778TSdI4PrrUFXd+WAOHyU4pNqwzYBdi6Y6NJRQUII90t84FMAXpo5hCsHdCKnsJSRf1nLveN78atR3Rs8py7/+dVlDO/ZwSmxVqeU2qq1Tq5rn0c0dwB0CAvkj5P6MrZvLECdCfrmoV1qPI4I9uelmRfzwk1DOLpwCqt/N5pQ2xI2r284Qmm568bXC8c6nVvMc1/sl+YrYbcHl/8IwPAe0Vw5wMzvHBkSwM5HJtaboAHuHtuz3n2uSNCN8ZiadHUfbk/jRFYh/TtHkNK7I2fyS4gI9ifQz5fcojL2nsrj1Q2HWfTzIbXWFSstt9J7nvnaEuzvy+4/XYGPG9uTRNNsPHiWm177tvLxJ3eNdMlMY6L1sVg1PeaalbvvHd+L51cfAOD2Ud14aIr9w74BNh46y95T57gksT1pOYX8+q0f+Mu0Acy4pEvjJztAQzVpj2y4vXZQ5xqPY8Kr2qwjgv25rHs0l3WPrvPc6l1lisosfLgjjesGx1NcZqHv/JUATB3cmWdnDHJC5KKp8kvKWfz1EWaP7EaAr0+NBA01R3w1RGvND8ez6RPXjtAAX6fO7ys8w23/+r7yfkWCBuh/QdP/qQ/v0YHhPUyteUB8BEcXTml5gA7iMc0djrRzwUT2PjYJMKv7fns4szJBAyzflsbqPenuCk9Uc8Vz63nmi/0s3Xys8htQdde++I1ds5ZNfG49017eRNIjq5j3wa56jzubX8K6fRkcPVtQY3tqdiFDn/iywc/FrrRcBj/6OWk5RRSUlLMrLZdX1x9m7d4MLFZNel4xxzLN82qteXD5j3x7OLPy/KyCUtLziut7egFsOHCG51fvJzO/pMHjMs4Vs27fmVrb1/z+cq4ddIGzwnMLj2zucJS+81cw/sLYWhcVK/Tr1I49p/L4+88Hc81A7/rFukNGXjEBfj5Ehtg3OusP/93Bf7fWHAXm76vYMm8CC1fs5e3vjtc6p3oN5/CZfO5/byffH609Yc62+ROICg0gI6+YqNAAzuaXsONELr9+a2vlMX//+WAWf3OE30/ow82vV9Xgtz88gSUbj/L86gP0jQtncJcoUrML2XDgbL1lCQ3wpaDUDH5IjA7haLWBWYt+Ppi73t5W+fip6y/ihuSEht6aNqvfwysptL2PFb/DulRc7LtjdHfuGteLbw9nMrJXBwL9fOs83tM11Nzh1Uk66ZFV5JeYcfmTk+J4+eaL2Xosi2kvb7Lr/MWzL2GMGzuxtybZBaUMfuwLAPx8FJEh/twzrhc3XtoFf9tS91prCkot7EzN4b53tpOeV7u2tGXeeG/azl0AAA6/SURBVDqEmVWY67rqvuGPZpKbJz77iRW7TtfYt+re0Xx/NKvBmnRz+PsqyixVfycdwgI4a+viNTAhkuOZBZRbNOdK6p8Doi6P/ywJgH4XtGNA54jK9+lccRkaXNY7aX/6OSY+t55lc4YytJ5mxJY4nVuMr4+qtbr2+dLzirnsiS9rbDu6cAqPfLiLf206RufIYLIKSukUGcThMwXEtgtk0wPjvOKaU5tN0lP+voHdJ/MAs5pCxUVGrTVvf3eCue//yMieHfj6YN01pO4dQ1nz+5Ra27XWFJdZa1209HZaaz7cfpIyi5WE9iGUWaxsOHCWJRuPNtiTJsjfh9tGduODbSdr9drZ8McxJLQPIa+4rFZSuuK59exLP9dgTAF+Pnxz/9jKBFD9wvH5Lu3WnqHdo/lNSg/+9PFu3v7uBN07hlJcamH+Vf2Y0C+Wxz/9iW3Hs+kdG84943sR1y4IXx+FUooTWYWEB/k1+k2htNzKsu+Pc0FEML96s+rv4YWbBtMzJoxJz2+o87yY8EC6dQjl2yNZAPToGMolie25eWhX4qOCKS230iEsEB8fhdaabw5mkp5XTEFpOd07hJFfUsY3BzMpLLWQGB1Cr9gw2ocGEhHsT0iAueieXVhKdmEZubbbnMIy1uxN52hmIbcM7cpjtn8cYH7fc9/fxe2juvGXlXtZtTudET2jGdA5kn98ZYZSf//QeAA+33Maq4bCknJuHZZY+bdRWFpeOdah4m9Qa13jmsGxzAJuf3ML+9PNElXv/b/hNcZA1MXfV7Hm9ykktHfPpEeO1maTdFpOEcu3ppKc2J5hPequIWQXlPLmpmOs3ZfB4lmXEBzgS2p2IX/6eA8bDpxlx8MTiQgxySOroJQNB85wz7LtADwzfSBTLupEkL/54J3JL6m8yLkrLZd/fHWIh6/uV+PCZ2uxbl8Gj36yh0EJkUSHBhAa6MfGQ5l8Z0sg5+sTG84zNwwkqXMExWUWtp/IYd4HuziYUbU2nK+PYsqATsS2C+S2kd2Ji2j8fdFac+fb2+jRIZS/21bHAPi/ib2ZNaIbYXUMWsouKGXRmoMUlpZz/6S+vLrhMHeN7eVR/1SXfnuMl9cdolNEEAlRIUSHBfBjWi4FJRZ+TMsFICTAt/KrfwU/H0W7YH/KLFbOFTet5l6X0ABfQgP9yDhnvtV8cd9oesWGA7D8h1R+9+6OZj3vyJ4dyC4srawk1SfA14fSaou8tgvyY8u8CRSVWhj46OeV20f37sirt17MC2sO0is23OuaJ9tskm6JD7enVSbjvnHhhAT48sPxnEbP81FQvUNCVIg/i2dfyqCESGeF2qjScitWrSkpsxLo70OQf+1klVNYSl5ROe2C/fjn+sO8vK7uSWfCAv2YnBRHaKAfYYF+HDqTz7Ae0dwytGu9PSq+O5JFucVKu2D/FnWnW7s3g1KLlXF9Y/Dz9cpr3rWs2ZvOscxCrBq+PnCGuAhTs7dYYUDnCPp2Cudgej5BAb74+yg6RwXTN64dRWUWTmQVkp5XTH5JOSXlViKD/YkKDSAy2J/IkAAigv0re0Ot25fBrMVVvSVuH9WN/3x7vLKdHcw3gdTsIg5l5DN5QBxFpVb+8L8dFJZauGN0d1L6xLBq92m2HMtiV1pVcl5wdT/KrZr3t6Wx51Qe56ecAF8ffjOmB9OGxNMpIqjyd7v9RA4Wq2ZgfITX/74lSTeD1aq54vn1HKhWE4xrF8SNlybQOTKYI2cL+GjHycrZtSqEBfqRX1JOz5gwLu4SxTtbTtTYHxrgS4+YMGLbBbErLZcbkhO4dVhXTucVk5pdxPYTOYQH+dEhNJBDZ/LZdiKH/OJyeseGMXVIPNmFpew7fY7ScisXdmpH/87t6B0TTrlVk19SzpubjrLnZB6B/r4E+PoQEezPG98cqXz9iGB/RvfuyP7T57jhkgRyi8o4kH6uVvsuwMd3jmRf+jk6RwYTFxFEdFiAjOL0Yi+sOcDTn++v0eY+oV8sL88c0uQkeTyzkE9+PMns4d3q/AajtcaqceucGJ5EknQzFZdZeObzffS7oB0XRAQzpGtU5cWd81msuvIDl5lfQrtgf/x9fTidW8zCFT/x0Y6TuHvw3PAe0ZzNL6ls+6uQ0D6YghILwf6+TOgXy7gLY7goPpKIYEnIbZHVqtmfcY7QAD/iIoLq/cwLx2lxklZKvQ70Az7VWj9e33HelqQdqSKJW6268sKPxapZf+AMH+84xZCuUfTr1I6u0SEczMgnrl0QoYF+RIb446sUW49ncygjn16xYSREhRAc4Mvb3x1n2/EcggN86RAWSHigH5d2a09cRBBd2oeQca6EsEA/fH1UjSaOknILFqvm6NlCYtoFVvamEEK4R4uStFJqKnCN1nqWUuoN4Emt9YG6jpUkLYQQTdfSCZZSgHdt9z8HRjooLiGEEI2wJ0mHAhXr0WQBsdV3KqXmKKW2KKW2nDlTe5imEEKI5rMnSecDFQuDhZ1/jtb6Fa11stY6uWPHjo6OTwgh2jR7kvRWqpo4BgJHnRaNEEKIGuyZqvQDYINS6gJgMjDUuSEJIYSo0GhNWmudh7l4uBkYo7XOdXZQQgghDLsm/ddaZ1PVw0MIIYSLOHTEoVLqDHDMYU/oGToA9U8k3Lp5a9mkXK2Pt5bN3nJ11VrX2fPCoUnaGymlttTXyby189aySblaH28tmyPKJYPyhRDCg0mSFkIIDyZJunGvuDsAJ/LWskm5Wh9vLVuLyyVt0kII4cGkJi2EEB5MkrQQQngwSdLnUUp57XuilPKclVhFo7z8s+iV62Y543fmtR+CplBKjVNKzVVKRWutrY2f0Tooo6NSajGA1trS2DmthVJqgFLKG/vVTlBK/UopFexNn0UApdQYpdQUAO1FF8OcnT/afJJWSv0V+A1mnuz7lFKd3RySw9j+EHyAyUqp68Grame/BaLcHYQjKaWeAh4EhgC/t21r1TXOiviVUncCc4HxSql7lFK93RtZy1Qr19M4OX94yx9sS2QBtwN/BPoDrfqPokK1ZBwHnAJmKaVCtNbW1tzsoZSqWB33OHCFUuoapdQIpVSEbX9r/v1lYD6L92CSmX9rrnEqpQKp+kcaDPxba30fppxXK6Xauy24FjivXGeAOTgxf7S5JK2USlJKPWG7HwJsAfK01iWYhN1q/8NXL1s1ZcCLmFkM74HW1+xRvVxa6zLb5jFAge3+NOB+2/5Wk9Tq+CwC3AHcCOwHblFKpbgpvBZRSs0GPgH+qpQaA5QDPrbKwzeABbjCjSE2S7VyLVRKTQS2ATnOzB9tLkkDPYGZSqkkrXWh1voLrXW5UioU6K21XgM1amytSUXZ+lVrG+sEJNpWeb9OKfWOUirefSE2S2W5qm3bCpzVWn8EPA10boVNVRXlGqC1LgTeBL4FEoGFQCQw0fbZbDWUUh2Ba4D/B+wCLsQsFjIYiMYsx3cMuKDaPyePd165fgIGa60/11pbnJk/vD5JK6W6KKVmKKU62TaFAO8DD1U7xh/zn36TUqqPUupxWsHiBg2UbX61wyxAqVLqWczyZ4la61QXh9okdparEFMuHyAeMzArDQ/W2GdRa52B+Rq9Rmt9GCgGOmutC+p8Qg9SrWxxmN/Hu1rrg8BXwPVa6/cx5ZkC+AK5wCjbPyeP1UC51mLKUtG0WA5sdEb+8OokrZRKAv4HXAQ8pJS6FPhQa30v5qvXdKj8Ct0b+D/gn0CG1nqDm8K2ix1lu8F2qB9wHbBLa90PeNR2vke23dpRrhm2Qz/DTAO5BHgK02zlsRopl6r2+0oH7lVKLQP+gmka8GjVyjYQ8zcUrrV+27Y7Czhiu/8GEI5pLrga2O3iUJukkXLlAHsBbN9aewN/wBn5Q2vtdT+YNso7MF+z3rRtuwK4D0ixPb4E84fuY3s8GngEaO/u+B1UthXVyhbu7rid9DtTwCggyt3xO/j31RO4GYhxd/zNLNv/AcNtj0cAz9ruJwFBwFRgAtDR3WVoYbmesd2/CFOrfsgZ+cOr5u5QSvkBr9seFmG+UingSaAUuBLzB7BIa52vlHoRyNJaz1dK+WoPvqDWzLJlaq0ftp3voz2w321Ly+WpWvJZdEe8TdGEsj2Fab9NxiTnDGCu1jrf1THbo5nlCsa0ry/UWmc5Iy5va+6wAoe01r/AfK0fiWlH6qlNu95eIABzYQZMG+daaBU9HppTtnUVJ3tigrZpUbk8WLM/i62APWULATpj+g8PB/6jtb7bUxO0TXPKtVRr/UdnJWjwviTtg7lQgdb6JLAHOAlcpZTqiXmTx2D+S6K1ztK2q7GtgLeWTcpFqyoX2Fe2UbZjl2ite2vTC8fTeWS57FqItrXQWpdje5OVUglAN631eKXUjcBjQDam+48n/zevk7eWTcrVusoFdpftFFCstT7uvkibxlPL5VVJuoKtS0wZpkvMhUAfYBXmTT6htU53Z3wt4a1lk3K1PnaULcOd8TWXx5XL3VdSnfWD6XRuBVYCt7o7HimblMvbyuXNZfOkcnlV747qbENRL8N0/yl1dzyO5K1lk3K1Pt5aNk8qlzcnaaW9tHDeWjYpV+vjrWXzpHJ5bZIWQghv4G1d8IQQwqtIkhZCCA8mSVq0ekqpJUqp7UqpLUqp2xs4LkUplei6yIRoOUnSwlvciZkE5xGl1EX1HJNC1TBsIVoFSdLCa2itM4FPgZ8ppb5WSm1QSv0ZQJnFeGcBzyulltq2xSqlViilNiqlHnRb4EI0QJK08DaZgAYeACZj5i1Gaz0bM/f0vVrrmbZjHwTe0VoPxyT2aNeHK0TDvHJYuGjT2gMnMEk6HzPJfH36AMOUUrOAUOACTJIXwmNIkhZeQykViak9f4eZA3gHsLPaIUWYqSYrVqbZh1kdZa1S6mbMKiJCeBQZzCJaPaXUEswSRyXA85jmjnnAGSAGuEJrnWabbvINzDzON2Nq2q9jFnw9AszSZiY0ITyGJGkhhPBgcuFQCCE8mCRpIYTwYJKkhRDCg0mSFkIIDyZJWgghPJgkaSGE8GCSpIUQwoP9f4st8fEfV4z/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(1+ret).cumprod().plot(label='stockRet')\n",
    "(1+tradeRet).cumprod().plot(label='tradeRet')\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Hold(signal):\n",
    "    hold = np.zeros(len(signal))\n",
    "    for index in range(1, len(hold)):\n",
    "        if hold[index - 1] == 0 and signal[index] == 1: # 没持仓， 买入\n",
    "            hold[index] = 1\n",
    "        elif hold[index - 1] == 1 and signal[index] == 1: # 有持仓， 买入\n",
    "            hold[index] = 1\n",
    "        elif hold[index - 1] == 1 and signal[index] == 0: # 有持仓， 卖出\n",
    "            hold[index] = 1\n",
    "        \n",
    "    return (pd.Series(hold, index=signal.index))\n",
    "            \n",
    "hold = Hold(trade)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 交易模拟函数\n",
    "def TradeSim(price, hold):\n",
    "    position = pd.Series(np.zeros(len(price)), index=price.index)\n",
    "    position[hold.index] = hold.values\n",
    "    cash = 20000 * np.ones(len(price))\n",
    "    \n",
    "    for t in range(1,len(price)):\n",
    "        if position[t - 1] == 0 and position[t] == 1:\n",
    "            cash[t] = cash[t-1] - price[t] * 1000\n",
    "        \n",
    "        if position[t - 1] == 1 and position[t] == 0:\n",
    "            cash[t] = cash[t-1] + price[t] * 1000\n",
    "            \n",
    "        if position[t-1] == position[t]:\n",
    "            cash[t] = cash[t - 1]\n",
    "            \n",
    "    asset = cash + price * position * 1000\n",
    "    \n",
    "    asset.name = 'asset'\n",
    "    account = pd.DataFrame({'asset': asset, 'cash': cash, 'postion': position})\n",
    "    return (account)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>asset</th>\n",
       "      <th>cash</th>\n",
       "      <th>postion</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-08-16</th>\n",
       "      <td>32620.0</td>\n",
       "      <td>32620.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-13</th>\n",
       "      <td>32620.0</td>\n",
       "      <td>32620.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-12</th>\n",
       "      <td>32620.0</td>\n",
       "      <td>32620.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-11</th>\n",
       "      <td>32620.0</td>\n",
       "      <td>32620.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-10</th>\n",
       "      <td>32620.0</td>\n",
       "      <td>32620.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              asset     cash  postion\n",
       "Date                                 \n",
       "2010-08-16  32620.0  32620.0      0.0\n",
       "2010-08-13  32620.0  32620.0      0.0\n",
       "2010-08-12  32620.0  32620.0      0.0\n",
       "2010-08-11  32620.0  32620.0      0.0\n",
       "2010-08-10  32620.0  32620.0      0.0"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TradeAccount = TradeSim(close, hold=hold)\n",
    "TradeAccount.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x00000186DD5120D0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00000186E9D177C0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00000186DD7B88B0>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "TradeAccount.plot(subplots=True, figsize=(16,8),title='成交量与均线策略交易账户')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2021-10-21    79.05\n",
       "2021-10-20    80.10\n",
       "2021-10-19    78.48\n",
       "2021-10-18    78.51\n",
       "2021-10-15    71.37\n",
       "              ...  \n",
       "2010-08-16    31.50\n",
       "2010-08-13    30.80\n",
       "2010-08-12    31.20\n",
       "2010-08-11    34.09\n",
       "2010-08-10    36.50\n",
       "Name: Close, Length: 2629, dtype: float64"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
